When using the conventional methods of pattern recognition and classification alone, it is difficult to set up the identification declarations with as exact error presentation (confidence statement) as possible, such that, with a higher degree of signal processing, this information can be combined as good as possible with regard to contradictional and uncertain contents of the information from individual sensors. In particular, the prior art methods involve long solution times for processing the multiple identification data, unavoidable errors and uncertainty of the data describing the target attributes. Furthermore, an error tolerance is required which cannot be achieved with the known techniques.
A particular problem is that the advanced sensor technology supplies increasingly comprehensive quantities of rough and also preprocessed sensor data which have to be processed in real time. In classical signal processing techniques, this leads to higher and higher requirements with regard to the transfer rate. This requires large computers which cannot be used in many applications.
Computers in the form of neural networks are known. Such neural networks offer the following advantages: They quickly reach satisfying solutions. They show a self-organized learning based on training with reference to examples. The learned rules and facts are stored in the weight factors of the processor element connections (long-time memory). Thereby, the wearisome communication between memory and processor elements necessary in conventional solutions is not necessary in neural networks. Parallel-redundant hardware realisation results in a highly failuretolerant mode of operation.
U.S. Pat. No. 4,876,731 deals with the recognition of patterns, in particular the reading of amounts on checks. The pattern is input as a pixel matrix. Signal processing is effected by means of a neural network. U.S. Pat. No. 4,876,731 also describes the use of an expert system in conjunction with object recognition for providing knowledge regarding, for example, to the laws of physics or to the type of the scene. In the embodiment of U.S. Pat. No. 4,876,731, knowledge about the amounts to be paid by the check are used to support identification of the pattern.
WO 90/16,038 describes a two-layer neural network which compares the position of a moving object, such as a rocket, with a predicted state.
German patent document 39 07 843 describes an expert system; German patent document 39 22 129 describes the setup of a neural computer.